首页|期刊导航|干旱区科学|Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains,China

Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains,China

LI Jiani XU Denghui XU Zhonglin WANG Yao YANG Jianjun

干旱区科学2026，Vol.18Issue(1)：56-83,28.

干旱区科学2026，Vol.18Issue(1)：56-83,28.DOI:10.1016/j.jaridl.2026.01.006

Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains,China

LI Jiani ¹XU Denghui ¹XU Zhonglin ¹WANG Yao ²YANG Jianjun¹

作者信息

1. College of Ecology and Environment,Xinjiang University,Urumqi 830017,China||Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,China||Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem,Ministry of Education,Urumqi 830017,China||Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis,Urumqi 830001,China
2. College of Ecology and Environment,Xinjiang University,Urumqi 830017,China||Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China
折叠

摘要

关键词

net primary productivity(NPP)/Carnegie–Ames–Stanford Approach(CASA)/Hurst exponent/land use change/Extreme Gradient Boosting(XGBoost)/SHapley Additive exPlanations(SHAP)/hydrothermal thresholds

Key words

引用本文复制引用

LI Jiani,XU Denghui,XU Zhonglin,WANG Yao,YANG Jianjun..Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains,China[J].干旱区科学,2026,18(1):56-83,28.

基金项目

This research was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023E01006,2024TSYCCX0004). （2023E01006,2024TSYCCX0004）

干旱区科学

ISSN：1674-6767

访问量0

下载量0

段落导航